Zobrazeno 1 - 10
of 600
pro vyhledávání: '"Rao Ashwin"'
Affective polarization, the emotional divide between ideological groups marked by in-group love and out-group hate, has intensified in the United States, driving contentious issues like masking and lockdowns during the COVID-19 pandemic. Despite its
Externí odkaz:
http://arxiv.org/abs/2412.14414
Autor:
Jiang, Bohan, Li, Dawei, Tan, Zhen, Zhou, Xinyi, Rao, Ashwin, Lerman, Kristina, Bernard, H. Russell, Liu, Huan
Measuring the relative impact of CTs is important for prioritizing responses and allocating resources effectively, especially during crises. However, assessing the actual impact of CTs on the public poses unique challenges. It requires not only the c
Externí odkaz:
http://arxiv.org/abs/2412.07019
Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also amplifies pseudo-experts promoting contrarian views. Despite its import
Externí odkaz:
http://arxiv.org/abs/2406.01866
Autor:
Anand, Abhishek, Mokhberian, Negar, Kumar, Prathyusha Naresh, Saha, Anweasha, He, Zihao, Rao, Ashwin, Morstatter, Fred, Lerman, Kristina
Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators. In this work we show that models that are only provided aggregated labels show low con
Externí odkaz:
http://arxiv.org/abs/2403.04085
Language models (LMs) are known to represent the perspectives of some social groups better than others, which may impact their performance, especially on subjective tasks such as content moderation and hate speech detection. To explore how LMs repres
Externí odkaz:
http://arxiv.org/abs/2402.11114
Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities. Existing research focused on probing ideological stances treats liberals and conservatives as separate groups. However, this fails to account
Externí odkaz:
http://arxiv.org/abs/2402.01091
The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using these data to understand social behavior is diff
Externí odkaz:
http://arxiv.org/abs/2401.06275
Social media platforms are rife with politically charged discussions. Therefore, accurately deciphering and predicting partisan biases using Large Language Models (LLMs) is increasingly critical. In this study, we address the challenge of understandi
Externí odkaz:
http://arxiv.org/abs/2311.09687
Members of different political groups not only disagree about issues but also dislike and distrust each other. While social media can amplify this emotional divide -- called affective polarization by political scientists -- there is a lack of agreeme
Externí odkaz:
http://arxiv.org/abs/2310.18553